An alternative trial-level measure for evaluating failure-time surrogate endpoints based on prediction error
Bibliographic record
Abstract
To validate a failure-time surrogate for an established failure-time clinical endpoint such as overall survival, the meta-analytic approach is commonly used. The standard correlation approach considers two levels: the individual level, with Kendall's τ measuring the rank correlation between the endpoints, and the trial level, with the coefficient of determination R2 measuring the correlation between the treatment effects on the surrogate and on the final endpoint. However, the estimation of R2 is not robust with respect to the estimation error of the trial-specific treatment effects. The alternative proposed in this article uses a prediction error based on a measure of the weighted difference between the observed treatment effect on the final endpoint and a model-based predicted effect. The measures can be estimated by cross-validation within the meta-analytic setting or external validation on a set of trials. Several distances are presented, with varying weights, based on the standard error of the observed treatment effect and of its predicted value. A simulation study was conducted under different scenarios, varying the number and the size of the trials, Kendall's τ and R2. These measures have been applied to individual patient data from a meta-analysis of trials in advanced/recurrent gastric cancer (20 randomized trials of chemotherapy, 4069 patients). The distance-based measures appeared to be robust with respect to different values of simulation parameters in several scenarios (such as Kendall's τ, size and number of clinical trials). The absolute prediction error can be an alternative to the trial-level R2 for evaluation of candidate time-to-event surrogates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.161 | 0.383 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".